What are the main causes of bias in AI algorithms used for loan approvals according to the chapter?

The main causes of bias in AI algorithms used for loan approvals include biased training data, the choice of features in the models, and the design of the algorithms themselves. Historical data may contain discriminatory patterns, leading to unfair treatment of certain groups. Additionally, factors like income level and geographic location can act as proxies for race, further perpetuating bias in decision-making.

Bias in AI algorithms for loan approvals arises from several key factors. First, the training data used to develop these algorithms often reflects past societal injustices, such as discrimination in lending practices like redlining. This means that if the historical data contains biases, the AI will likely reproduce those biases in its decisions. Second, the selection of features used in the models can inadvertently include variables that correlate with sensitive attributes like race or gender, even if those attributes are not directly included. For example, ZIP codes or income levels can serve as proxies for race, leading to biased outcomes. Lastly, the design of the algorithms themselves may prioritize accuracy over fairness, resulting in decisions that disadvantage certain groups. These biases can perpetuate inequality in loan approvals and other financial services.